Parallel linear complementarity solver for rigid body dynamics

ABSTRACT

A system and method for solving linear complementarity problems for rigid body simulation is disclosed. The method includes determining one or more contact constraints affecting an original object having an original mass. The method includes splitting the original object by a total number of the contact constraints into a plurality of sub-bodies. The method includes assigning a contact constraint to a corresponding sub-body. The method further includes solving contact constraints in isolation for each sub-body. The method also includes enforcing positions and orientations of each sub-body are identical.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Non-Provisional applicationSer. No. 13/774,906, filed on Feb. 22, 2013, which is herebyincorporated by reference in its entirety.

This application is related to concurrently filed U.S. Non-Provisionalapplication Ser. No. 13/775,003, entitled “MODIFIED EFFECTIVE MASS FORPARALLEL RIGID BODY SIMULATION,” filed on Feb. 22, 2013, which is hereinincorporated by reference in its entirety.

BACKGROUND

Rigid body dynamics is widely used in applications ranging from moviesto engineering to video games. Piles of objects are particularly common,because ultimately, gravity pulls all rigid bodies to the ground. Someof the most visually interesting simulations involve destruction, suchas projectile impacts and explosions, and these can generate large pilesof debris. In mechanical engineering some of the most computationallychallenging problems involve simulating interaction with large restingsystems of soil particles or rocks. Piles require stable simulation ofstatic friction, dynamic friction and resting contact, which presentmany challenges.

In large-scale or real-time simulations, the time available forcomputation can be small compared to the number of rigid body contacts.In these cases iterative methods are used to solve rigid body dynamics.However, iteration is often terminated well before convergence tomaximize the rigid body count. This results in residual energy, whichcan cause artifacts when its distribution over the contacts varies toomuch from frame to frame. The effect of this can be seen when bodiescoming to rest oscillate (jitter) or move around each othernon-physically (swim).

A commonly used iterative algorithm for simulating interactions isserial projected Gauss-Seidel (PGS), which solves contacts in sequence.When the iteration is terminated, the last contact solved has no errorand the other contacts have non-zero error, resulting in an unevendistribution of the residual energy. On single threaded implementationsthe distribution of this error can be made consistent from frame toframe by, for example, by ensuring that collisions are detected in thesame order each frame and that addition and deletion of bodies do notdisrupt the order.

Unfortunately, things are not as straightforward for parallelimplementations. PGS has limited parallelism, as updates to bodieshaving multiple contacts must be serialized to ensure they are not lostand the connectivity between bodies can be complex, especially in piles.It is very challenging to avoid jittering and swimming with parallelPGS, and due to serialization its performance does not scale well asmore threads are added.

SUMMARY

A computer implemented method and system for a parallel iterative rigidbody solver that simulates complex systems coming to rest while avoidingartifacts such as, jittering and swimming at very low iteration counts.The systems and methods deal with resolution of contacts after contactshave been detected. The parallel iterative solver andcomputer-implemented method for implementation distributes the residualamong the contacts consistently from frame to frame eliminating swimmingand visible jittering. This improves simulation quality in use caseswhere the iteration is stopped well before convergence withoutintroducing swimming or visible jittering. It utilizes one thread percontact without requiring serialization, resulting in a low cost periteration and providing excellent parallel scaling. In one embodiment,the method includes splitting the mass of each body between its contactsbefore solving the contacts independently. In another embodiment, themethod splits the bodies so that each sub-body receives a block ofcontacts with the corresponding portion of the mass in order toaccelerate convergence. The system solver and method provides forcorrect momentum propagation, static friction, dynamic friction andstable resting contact.

In one embodiment, a computer implemented method for solving linearcomplementarity problems for rigid body simulation is disclosed. Themethod includes providing, receiving and/or determining one or morecontact constraints affecting an original object having an originalmass. The method includes splitting the original object by a totalnumber of the contact constraints into a plurality of sub-bodies. Themethod includes assigning a contact constraint to a correspondingsub-body. The method further includes solving contact constraints inisolation. The method also includes adding fixed joints between thesub-bodies, and enforcing them exactly, such that positions and/orvelocities of each sub-body are identical.

In another embodiment, another method for solving linear complementarityproblems for rigid body simulation or solving contact constraints forsystems of rigid bodies is disclosed. The computer implemented methodincludes determining one or more patches associated with an originalobject having an original mass. The patch comprises a pair of collidingobjects including the original object and the patch defines a pluralityof contact constraints affecting the pair of colliding objects. Themethod includes splitting the original object by a total number of thepatches into a plurality of sub-bodies. Each sub-body is assigned to apatch and comprises a sub-body mass defined by the original mass dividedby the total number of the patches. The method also includes solvingpatches in parallel, such that for each patch, in sequential fashioncontact constraints are solved approximately. The method also includesadding fixed joints between the sub-bodies, and enforcing them exactly,such that positions and/or velocities of each sub-body are identical.

In still another embodiment, a linear complementarity solver for rigidbody simulation is disclosed. The solver includes a patch determiningmodule for determining one or more patches associated with an originalobject having an original mass, wherein a patch comprises a pair ofcolliding objects including the original object and the patch defines aplurality of contact constraints affecting the pair of collidingobjects. The solver includes an object splitter for splitting theoriginal object by a total number of the patches into a plurality ofsub-bodies. Each sub-body is assigned to a patch and comprises asub-body mass defined by the original mass divided by the total numberof patches. The solver also includes a contact constraint solver forsolving patches in parallel, such that for each patch, in sequentialfashion contact constraints are solved approximately. The solver alsoincludes a fixed joint solver for enforcing fixed joint constraintsexactly, such that positions and/or velocities of each sub-body areidentical.

These and other objects and advantages of the various embodiments of thepresent disclosure will be recognized by those of ordinary skill in theart after reading the following detailed description of the embodimentsthat are illustrated in the various drawing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and form a part ofthis specification and in which like numerals depict like elements,illustrate embodiments of the present disclosure and, together with thedescription, serve to explain the principles of the disclosure.

FIG. 1 depicts a block diagram of an exemplary computer system suitablefor implementing the present methods, in accordance with one embodimentof the present disclosure.

FIG. 2 is a block diagram of an exemplary system for performing rigidbody simulation, in accordance with one embodiment of the presentdisclosure.

FIG. 3 is a block diagram of a linear complementarity, paralleliterative rigid body solver that implements a scalar method forsimulating complex systems coming to rest while avoiding artifacts suchas, jittering and swimming at very low iteration counts, in accordancewith one embodiment of the present disclosure.

FIG. 4 is a flow diagram illustrating a scalar method for solving linearcomplementarity iterative rigid body dynamics, in accordance with oneembodiment of the present disclosure.

FIG. 5 is a diagram illustrating coloring for a parallel projectedGauss-Seidel solution for rigid body dynamics.

FIG. 6 is a diagram illustrating the splitting of the mass of anoriginal object into one or more sub-bodies, in accordance with oneembodiment of the present disclosure.

FIG. 7 is a block diagram of a linear complementarity, paralleliterative rigid body solver that implements a block method forsimulating complex systems coming to rest while avoiding artifacts suchas, jittering and swimming at very low iteration counts, in accordancewith one embodiment of the present disclosure.

FIG. 8 is a flow diagram illustrating a block method for solving linearcomplementarity iterative rigid body dynamics, in accordance with oneembodiment of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the various embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. While described in conjunction with theseembodiments, it will be understood that they are not intended to limitthe disclosure to these embodiments. On the contrary, the disclosure isintended to cover alternatives, modifications and equivalents, which maybe included within the spirit and scope of the disclosure as defined bythe appended claims. Furthermore, in the following detailed descriptionof the present disclosure, numerous specific details are set forth inorder to provide a thorough understanding of the present disclosure.However, it will be understood that the present disclosure may bepracticed without these specific details. In other instances, well-knownmethods, procedures, components, and circuits have not been described indetail so as not to unnecessarily obscure aspects of the presentdisclosure.

Some portions of the detailed descriptions that follow are presented interms of procedures, logic blocks, processing, and other symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the means used by thoseskilled in the data processing arts to most effectively convey thesubstance of their work to others skilled in the art. In the presentapplication, a procedure, logic block, process, or the like, isconceived to be a self-consistent sequence of steps or instructionsleading to a desired result. The steps are those utilizing physicalmanipulations of physical quantities. Usually, although not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated in a computer system. It has proven convenient at times,principally for reasons of common usage, to refer to these signals astransactions, bits, values, elements, symbols, characters, samples,pixels, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the present disclosure,discussions utilizing terms such as “splitting,” “solving,” “enforcing,”“assigning,” “determining,” or the like, refer to actions and processes(e.g., flowcharts 400 and 800 of FIGS. 4 and 8, respectively) of acomputer system or similar electronic computing device or processor(e.g., system 100, 200, 300, and 700 of FIGS. 1, 3 and 7 respectively).The computer system or similar electronic computing device manipulatesand transforms data represented as physical (electronic) quantitieswithin the computer system memories, registers or other such informationstorage, transmission or display devices.

FIGS. 4 and 8 are flowcharts of examples of computer-implemented methodsfor processing data according to embodiments of the present invention.Although specific steps are disclosed in the flowcharts, such steps areexemplary. That is, embodiments of the present invention are well-suitedto performing various other steps or variations of the steps recited inthe flowcharts.

Embodiments of the present invention described herein are discussedwithin the context of hardware-based components configured formonitoring and executing instructions. That is, embodiments of thepresent invention are implemented within hardware devices of amicro-architecture, and are configured for monitoring for critical stallconditions and performing appropriate clock-gating for purposes of powermanagement.

Other embodiments described herein may be discussed in the generalcontext of computer-executable instructions residing on some form ofcomputer-readable storage medium, such as program modules, executed byone or more computers or other devices. By way of example, and notlimitation, computer-readable storage media may comprise non-transitorycomputer storage media and communication media. Generally, programmodules include routines, programs, objects, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. The functionality of the program modules may becombined or distributed as desired in various embodiments.

Computer storage media includes volatile and nonvolatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, random access memory (RAM), read only memory (ROM),electrically erasable programmable ROM (EEPROM), flash memory or othermemory technology, compact disk ROM (CD-ROM), digital versatile disks(DVDs) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium that can be used to store the desired information and that canaccessed to retrieve that information.

Communication media can embody computer-executable instructions, datastructures, and program modules, and includes any information deliverymedia. By way of example, and not limitation, communication mediaincludes wired media such as a wired network or direct-wired connection,and wireless media such as acoustic, radio frequency (RF), infrared andother wireless media. Combinations of any of the above can also beincluded within the scope of computer-readable media.

FIG. 1 is a block diagram of an example of a computing system 100capable of implementing embodiments of the present disclosure. Computingsystem 10 broadly represents any single or multi-processor computingdevice or system capable of executing computer-readable instructions.Examples of computing system 100 include, without limitation,workstations, laptops, client-side terminals, servers, distributedcomputing systems, handheld devices, or any other computing system ordevice. In its most basic configuration, computing system 100 mayinclude at least one processor 110 and a system memory 140.

Both the central processing unit (CPU) 110 and the graphics processingunit (GPU) 120 are coupled to memory 140. System memory 140 generallyrepresents any type or form of volatile or non-volatile storage deviceor medium capable of storing data and/or other computer-readableinstructions. Examples of system memory 140 include, without limitation,RAM, ROM, flash memory, or any other suitable memory device. In theexample of FIG. 1, memory 140 is a shared memory, whereby the memorystores instructions and data for both the CPU 110 and the GPU 120.Alternatively, there may be separate memories dedicated to the CPU 110and the GPU 120, respectively. The memory can include a frame buffer forstoring pixel data drives a display screen 130.

The system 100 includes a user interface 160 that, in oneimplementation, includes an on-screen cursor control device. The userinterface may include a keyboard, a mouse, and/or a touch screen device(a touchpad).

CPU 110 and/or GPU 120 generally represent any type or form ofprocessing unit capable of processing data or interpreting and executinginstructions. In certain embodiments, processors 110 and/or 120 mayreceive instructions from a software application or hardware module.These instructions may cause processors 110 and/or 120 to perform thefunctions of one or more of the example embodiments described and/orillustrated herein. For example, processors 110 and/or 120 may performand/or be a means for performing, either alone or in combination withother elements, one or more of the monitoring, determining, gating, anddetecting, or the like described herein. Processors 110 and/or 120 mayalso perform and/or be a means for performing any other steps, methods,or processes described and/or illustrated herein.

In some embodiments, the computer-readable medium containing a computerprogram may be loaded into computing system 100. All or a portion of thecomputer program stored on the computer-readable medium may then bestored in system memory 140 and/or various portions of storage devices.When executed by processors 110 and/or 120, a computer program loadedinto computing system 100 may cause processor 110 and/or 120 to performand/or be a means for performing the functions of the exampleembodiments described and/or illustrated herein. Additionally oralternatively, the example embodiments described and/or illustratedherein may be implemented in firmware and/or hardware.

FIG. 2 is a block diagram of a rigid body physics system or engine 200for performing rigid body simulation, in accordance with one embodimentof the present disclosure. In particular, system 200 calculates themotion of a set of bodies such that when the bodies are displayed on ascreen, they appear to obey the laws of physics and don't pass througheach other.

As shown in FIG. 2, at each displayed frame, a physics system 200executes a sequence of stages (e.g., a pipeline) to calculate new thepositions and orientations of the bodies. For instance, the pipeline ofsystem 200 includes a collision detector 210 that takes as inputs shapesand poses of objects, and outputs contacts that describe theinteractions of contacting pairs of objects. Each contact can berepresented as a constraint that constrains the relative motion of pairsof contacting bodies. A constraint generator 220 takes each contact andconverts it to a constraint representation that can be solved by asolver. That is, generator 220 outputs constraints. Block 230 takes theinitial velocities of the objects and applies external forces orimpulses affecting the objects. A vector of velocities is output fromblock 230. Solver 250 takes the set of constraints that the velocities,positions, and/or orientations of bodies must satisfy and calculates aset of corrections such that the bodies will satisfy the constraintsonce the corrections are applied to the bodies. In one embodiment,solver 250 calculates position corrections and velocity corrections. Inanother embodiment, solver 250 calculates only velocity corrections. Inone embodiment, velocities are corrected with impulses. In anotherembodiment, velocities are corrected with forces.

In embodiments of the present invention, solver 250 solves a linearcomplementarity problem. The solver 250 is configured to process inparallel operations needed to simulate a very large number of rigidbodies at real-time frame rates (e.g., 60 Hz). As such, the desiredframe rate is achieved using the parallel linear complementarity solver250, which is running on a parallel processor or processors (e.g., GPU,multi-core CPU, multi-threaded processor, etc.).

Scalar Method and System for a Rigid Body Solver

FIG. 3 is a block diagram of a linear complementarity, paralleliterative rigid body solver 300 for simulating complex systems coming torest while avoiding artifacts such as, jittering and swimming at verylow iteration counts, in accordance with one embodiment of the presentdisclosure. In one embodiment, solver 300 performs the functions ofsolver 250 of FIG. 2 within the rigid body physics system or engine 200.

Solver 300 includes an object mass splitter 310, for splitting anoriginal object into a plurality of sub-bodies. The bodies are not splitspatially, as performed by traditional finite element methods, butrather the mass is split so that all the sub-bodies have the samespatial extents, center of mass and initial velocity. In one embodiment,the splitter 310 also splits the original mass of the original object,such that each sub-body has the same mass distribution. Moreparticularly, solver 300 is configured to split the original object by atotal number of contact constraints that are affecting the originalobject.

Solver 300 also includes a contact constraint assigner 315. Each contactconstraint is assigned to a corresponding sub-body. In that manner, bysplitting the original object into one or more sub-bodies, multiplecomputing threads are used to compute solve contact constraint inparallel. That is, contact constraint solver 320 is configured to solvecontact constraints approximately for each sub-body. Further, eachcontact constraint is solved with respect to its corresponding sub-bodyin isolation. As such, contact constraint assigner 315 is configured touse a relative velocity at the contact point as defined by the contactconstraint and calculate an impulse applied at that point to eliminatethe relative velocity.

Solver 300 also includes a fixed joint solver 325 that enforces fixedjoint constraints exactly, such that positions and orientations of eachsub-body are identical. With multiple sub-bodies, applying an impulse atone contact point for one constraint can affect the velocity at manyother contact points. That is, after the sub-bodies have been computed,the impulse results are combined to find the force on the original body.The combination of sub-bodies is modeled as a fixed joint that forcesall the sub-bodies of the original object to have the same position,orientation, and velocity. That is, in one embodiment, by splitting theoriginal object into a plurality of sub-bodies, the fixed joint isenforced or solved at each iteration by averaging the sub-bodyvelocities, positions and/or orientations and applying the averagevelocity to each of the sub-bodies. The average velocity is used as theinitial velocity of the sub-bodies in the next iteration to solvecontact constraints and fixed joint constraints

FIG. 4 is a flow diagram 400 illustrating a computer implemented scalarmethod for solving linear complementarity iterative rigid body dynamics,in accordance with one embodiment of the present disclosure. In anotherembodiment, flow diagram 400 is implemented within a computer systemincluding a processor and memory coupled to the processor and havingstored therein instructions that, if executed by the computer systemcauses the system to execute a scalar method for solving linearcomplementarity iterative rigid body dynamics. In still anotherembodiment, instructions for performing a method are stored on anon-transitory computer-readable storage medium havingcomputer-executable instructions for causing a computer system toperform a scalar method for solving linear complementarity iterativerigid body dynamics. In one embodiment, flow diagram 400 is implementedby solver 300 of FIG. 3, and/or solver 250 of FIG. 2. More particularly,flow diagram 400 is implemented to take a set of constraints that thevelocities, positions, and/or orientations of objects must satisfy andcalculate a set of forces and/or impulses such that the objects willsatisfy the constraints once the forces and/or impulses are applied tothe objects.

The method of flow diagram 400 is implemented after a set of constraintsare defined, wherein contact constraints affect one or more objects orbodies. For instance, constraint generator 220 of FIG. 2 is able todetermine the constraints that affect one or more objects or bodies.Once the constraints are defined, the scalar method of flow diagram 400is implemented to determine the positions, orientations, and/orvelocities of the one or more objects or bodies.

At 410, splitting of an original object is performed, wherein theoriginal object is associated with an original mass. The original objectis split into a plurality of sub-bodies by splitting the original objectby a total number of contact constraints affecting the original object.Additionally, the sub-bodies all have the same spatial extent, center ofmass, and initial velocity.

In one embodiment, each of the sub-bodies have the same massdistribution. That is, each of the sub-bodies have an equally dividedmass that is defined by dividing the original mass by the total numberof contact constraints. In another embodiment, the masses of eachsub-body may be assigned to varying values, such that the masses of twosub-bodies may be different.

At 420, assigning each contact constraint to a corresponding sub-body isperformed. By splitting into sub-bodies, different computing threads areused to compute different contact constraints in parallel. As such,parallel computations may be performed for solving contact constraintsand solving fixed joint constraints, as provided in 430 and 440 below.

At 430, solving contact constraints is performed. In addition, thesolving of contact constraints is performed in isolation for eachsub-body. The affect one sub-body has on another sub-body is consideredwhen solving for fixed joint constraints at 440.

In one embodiment, the velocity Signorini condition is satisfied whensolving the contact constraints. That is, a contact constraint is solvedbetween a pair of sub-bodies, such that impulses are applied in pairs toeach sub-body, that have equal magnitudes but opposite directions inorder to satisfy Newton's third law of motion. In that manner, themagnitude of the pair of impulses is solved. Applying the pair ofimpulses changes the relative velocity of the pair of sub-bodies at thecontact point. The corresponding contact constraint imposes requirementson both the magnitude and the resulting relative velocity, as follows:the resulting relative velocity must be zero or positive (separating);and the magnitude of the impulse must be zero or positive (repulsive),wherein the velocity must be zero when the impulse is repulsive, and theimpulse must be zero when the relative velocity is separating.

At 440, fixed joint constraints are solved exactly. More particularly,positions and/or velocities of each sub-body are solved such that theyare substantially identical. For instance, in one embodiment, theenforcing of fixed joints is performed by averaging a plurality ofsub-body velocities corresponding to the plurality of sub-bodies. Assuch, an average velocity is defined. Further, the average velocity isapplied to each of the plurality of sub-bodies.

In one embodiment, the operations in 430 and 440 are iterativelyperformed. That is, for each iteration, given a starting averagevelocity from the previous iteration, a new average velocity and newposition and/or new orientations are determined for a correspondingsub-body. These new values again correspond to an average velocity andposition and/or orientation for the original object that can be usedfinally or for the next iteration.

In another embodiment, a frictional force is applied to determine thesub-body velocity at each iteration. Embodiments of the presentdisclosure support any means for applying friction. Embodiments of thepresent disclosure apply a coulomb friction model, though other frictionmodels are supported. For instance, friction may be considered byapplying a friction model, such that the more force an object isexerting on a surface, the harder it needs to be pushed to move itacross the surface. As such, heavier objects are harder to push thanlighter objects. In addition, the transition from sticking (e.g., whenthe object does not move) to sliding (e.g., when the object first moves)is considered.

The solution for the scalar method for performing linear complementarityin parallel for an iterative rigid body solver is described below. Aspreviously described, the scalar method simulates complex systems comingto rest while avoiding artifacts such as, jittering and swimming at verylow iteration counts. Portions described below are equally adaptable foruse in the block method for performing linear complementarity inparallel for an iterative rigid body solver that is further describedbelow in relation to FIGS. 7 and 8, in other embodiments.

Rigid Body Contact Dynamics

The rigid body contact model used is summarized. Consider a scene havingn rigid bodies with positions ϵ

^(6n), external forces f_(e) ϵ

^(6n) and masses M ϵ

^(6n×{circumflex over (6)}n). Collision detection identifies m contactsbetween the rigid bodies, represented by constraints Φ(x)≥0 withJacobian ∂Φ/∂x=J ϵ

^(m×6n). For simplicity of description constraint stabilization isomitted, so the unknowns are the forces λδ ϵ

^(m) necessary to satisfy the time derivative of the constraints.Friction is treated separately, in one embodiment.

Contacts must satisfy the velocity Signorini condition: i.e., forcesmust not be attractive (e.g., λ≥0), velocities must move the system outof penetration

$\left( {{\frac{\partial\Phi}{\partial x}v} \geq 0} \right),$

and a force should be applied at a contact only if that contact is notseparating, which is expressed as λ≥0⊥Jv≥0. Putting this all together,the continuous model is described in the following equations.

M{umlaut over (x)}=J ^(T) λ+f _(e)   (1)

{grave over (x)}=v   (2)

λ≥0⊥Jv≥0.   (3)

Time Stepping

The model is discretized using a semi-implicit stepping scheme with timestep h, and constraint impulse z=hλ, as described in the followingequations:

M(v _(new) −v _(old))=J ^(T) z+hf _(e)   (4)

x _(new) −x=hv _(new)   (5)

z≥0⊥Jv _(new)≥0.   (6)

The Signorini condition causes the discretized model for rigid bodydynamics to be a linear complementarity (LCP) formulation rather than alinear system. As such, let x:=LCP(A, b) be defined in the followingequation:

Find x ϵ

^(m) such that, for all i=1 . . . m. x _(i)≥0 and (Ax+b)_(i)≥0 and x_(i)=0 or (Ax+b)_(i)=0.   (7)

As such, the discretized model is solved in the following equations:

v ⁰ =v _(old) +hM ⁻¹ f _(e)   (8)

q=Jv ⁰   (9)

N=JM ⁻¹ J ^(T)   (10)

z=LCP(N,q).   (11)

Two iterative methods can be used to solve the rigid body LCP: projectedGauss-Seidel (PGS) and projected Jacobi. Both these methods aredescribed below.

Projected Guass-Seidel

The projected Guass-Seidel model solves each constraint individually insequence. Let L, U, D be the lower triangle, upper triangle and diagonalof N respectively. Abstractly PGS generates the following iteration froman initial guess, z⁰, in the following equations:

z ^(r+1)=(z ^(r) −D ⁻¹(q+Lz ^(r+1) +Uz ^(r)))⁺  (12)

(x ⁺)_(i)

max(0,x _(i)).   (13)

Forming N explicitly would take 0(m³) space and 0(m²) time, which isoften infeasible as m can be very large ≥(10000). The formation of N maybe avoided by explicitly updating the body velocities at each iteration,maintaining the invariant v=M⁻¹J^(T)+v_(old), and calculating Jv whenelements of Nz are required, which is shown in Algorithm 1, below:

Algorithm 1 PGS iteration optimized for N = JM⁻¹J^(T) 1: for allcontacts, i do 2. z_(i) = max(0, z_(i) − M_(ii) ⁻¹(Jv)_(i) 3. v =M⁻¹J^(T)z 4.  end for

Each contact i constrains two bodies, let b_(i1) be the index of thefirst body, and b_(i2) be the index of the second. Each row of Jcorresponds to a contact and the (six element wide) column blocks of Jcorrespond to the bodies constrained by each row, so J is sparse withtwo groups of six contiguous elements per row. By taking advantage ofthis sparsity, the iteration cost becomes 0(m) space and 0(m) time,which is described in Algorithm 2, below:

Algorithm 2 PGSiter(z,M,J,v) 1: for all contacts, i do 2. t = z_(i) 3.z_(i) = max(0,z_(i) − M_(ii) ⁻¹(Jv)_(i) 4. for all j ∈ {1,2} do 5. β =b_(i,j) 6. v_(β) = v_(β) + M_(β) ⁻¹(J_(iβ))^(T)(z_(i) − t ) 7. end for8. end for

Parallel Projected Gauss-Seidel

Each body is often in contact with more than one other body, so if allthe contacts are processed in parallel, then each body velocity will beupdated by many contacts simultaneously, which in turn causes velocityupdates to be lost, thereby adversely affecting convergence. Onesolution is contact coloring, which partitions (or colors) the contactsso that each body is referenced at most once in each partition. The PGSsolver can then process each color sequentially, processing the contactswithin each color in parallel.

FIG. 5 is a diagram illustrating coloring for a parallel projectedGauss-Seidel solution for rigid body dynamics. As shown, five blocks areshown in various contact with each other and a surface 540. Forinstance, Block-A is in contact with surface 540 at R-0 and Block-D atG-3; Block-B is in contact with surface 540 at R-1, Block-D at B-4, andBlock-E at G-5; Block-C is in contact with surface 540 at R-2, andBlock-E at B-6; Block-D is in contact with Block-A at G-3, and Block-Bat B-4; and Block-E is in contact with Block-B at G-5, and Block-C atB-6. Three colors are used to partition out the contacts so that eachbody is referenced at most in one partition, as follows: Red as “R,”Green as “G,” and Blue as “B.” As such, the constraints are processed bypartition, such as, processing R-0, R-1, and R-2 first, then processingG-3 and G-5, and then processing B-4 and B-6 last.

The number of colors required depends on Δ, the maximum number ofcontacts per body over all the bodies. The minimum number of colorsrequired is either Δ or Δ+1. A parallel algorithm is used to find theoptimal coloring, which gives at most 2Δ−1 colors. In piles containingobjects of very different sizes, or long thin objects like planks ofwood, Δ can be quite high. In the example with planks of wood, there maybe up to 10 contacts per body and the greedy coloring algorithm mayproduce 16 colors. Generally, the first few colors contain a largeproportion of the contacts, but the following colors contain fewer andfewer contacts. As the colors must be processed sequentially, the timetaken by each iteration is multiplied by the number of colors, reducingthe speedup of parallel PGS versus serial PGS. As such, on architectures(like graphic processing units [GPUs]) with hundreds of cores, most ofthem are idle for most of the iteration, especially as back end colorsare processed. In embodiments of the present disclosure, processors areused to process contact constraints for one or more sub-bodies, and assuch this unused processing power is harnessed to reduce swimming andjitter.

Parallel Projected Jacobi

In the parallel projected Jacobi method, each thread takes a constraintand calculates the impulse required to satisfy the constraintcompletely. Formally, the iteration is calculated in the followingequation:

z ^(r+1)=(z ^(r) −D ⁻¹(Nx ^(r) +q))⁺.   (14)

Mass Splitting

In embodiments of the present disclosure, each body is split intosub-bodies so that each sub-body has one contact. In one implementation,the mass is split evenly between the sub-bodies. In anotherimplementation, the mass is split unevenly. As such, each sub-body issolved independently, and then joined back together after havingprocessed all the contacts and their contact constraints.

A common way in non-rigid simulation is to split bodies spatially, as isdone when simulating deformables using a finite element method. However,this would cost time and memory and would lead to a complicated method.On the other hand, in embodiments of the present disclosure, a body issplit into sub-bodies that occupy the same region of space as theoriginal body. Each sub-body has the same mass distribution as theoriginal body, in one embodiment. Hence, each sub-body moment of inertiais just a scalar fraction of the original moment of inertia. The body issplit so that each sub-body has just one contact. Fixed joints are addedto force all the sub-bodies of each body to have the same positionand/or velocity.

Splitting the Bodies

The split system is constructed from the original system. FIG. 6 is adiagram 600 illustrating the splitting of the mass of an original objectinto one or more sub-bodies, in accordance with one embodiment of thepresent disclosure. As such, original object 610 is split intosub-bodies 610A and 610B. A fixed joint constraint joins the sub-bodiesback together. Also shown in FIG. 6 is the original object 610 incontact with a non-moving block 640 and a moveable object, such as, aconcrete block 630. For instance, contact patch 650 represents thecontact formed between original object 610 and non-moving block 640.Contact patch 660 represents the contact formed between original object610 and moving block 630. Further, sub-bodies are formed and assigned toa contact or contact constraint. For instance, contact patch 650represents the contact formed between sub-body 610A and non-moving block640. Also, contact patch 660 represents the contact formed betweensub-body 610B and moving block 660.

Continuing, let n_(β) be the number of constraints affecting originalbody β. We split body β into n_(β) sub-bodies, so the split system willhave sub-bodies as described in Eqn. 15.

$\begin{matrix}{n_{s} = {\sum\limits_{\beta = 1}^{n}\; n_{\beta}}} & (15)\end{matrix}$

The mass and inertia of body β is split evenly between its n_(β)sub-bodies, in one embodiment. As such, the inverse mass matrix of thesub-bodies of body β is described in Equation 16, as follows:

$\begin{matrix}{{W_{\beta} = {\begin{bmatrix}{n_{\beta}M_{\beta}^{- 1}} & \; & 0 \\\; & \ddots & \; \\0 & \; & {n_{\beta}M_{\beta}^{- 1}}\end{bmatrix} \in {\mathbb{R}}^{6n_{\beta} \times 6n_{\beta}}}},} & (16)\end{matrix}$

The inverse mass matrix of the whole split system is described inEquation 17, as follows:

$\begin{matrix}{{W = \begin{bmatrix}W_{1} & \; & 0 \\\; & \ddots & \; \\0 & \; & W_{n}\end{bmatrix}},} & (17)\end{matrix}$

Each sub-body is just a portion of the mass of the original body, so theinitial velocity of each sub-body of body β is set to the initialvelocity of the original body β, as described in Equation 18, asfollows:

$\begin{matrix}{{v_{\beta} = {\begin{bmatrix}v_{\beta} \\\vdots \\v_{\beta}\end{bmatrix} \in {\mathbb{R}}^{6n_{i}}}},} & (18)\end{matrix}$

The velocity vector for the whole split system is described in Equation19, as follows:

$\begin{matrix}{{v^{S} = \begin{bmatrix}v_{1} \\\vdots \\v_{n}\end{bmatrix}},} & (19)\end{matrix}$

The unconstrained velocity, v^(S0) is constructed from v^(S) in the sameway.

Joining the Bodies Back Together

A particular type of bilateral constraint is a fixed joint, a jointwhich forces the position and orientation of two bodies to be the same.Fixed joints are not commonly used, as it would be wasteful to calculateimpulses to join two bodies when they could be replaced by a singlebody. However, during the derivation of the method, impulses willdisappear in the final algorithm. Fixed joints are represented by theconstraint function, as described in Equation 20, as follows:

$\begin{matrix}{{\Phi^{FJ}\begin{pmatrix}x_{1} \\x_{2}\end{pmatrix}} = {{x_{1} - x_{2}} = 0}} & (20)\end{matrix}$

The Jacobian of the function is described in Equation 21, as follows:

$\begin{matrix}{J^{FJ} = {\frac{\partial\Phi^{FJ}}{\partial x} = \left\lbrack {I - I} \right\rbrack}} & (21)\end{matrix}$

For each body β, fixed joints n_(β)−1 are introduced to join thesub-bodies together. This gives a total number of fixed joints, m_(f),as expressed in Equation 22, as follows:

$\begin{matrix}{m_{f} = {\sum\limits_{\beta = 1}^{n}\; {\left( {n_{\beta} - 1} \right).}}} & (22)\end{matrix}$

The Jacobian of the fixed joints of body β is described in Equation 23,as follows:

$\begin{matrix}{F_{\beta} = {\begin{bmatrix}I_{6} & {- I_{6}} & 0 & \ldots & 0 \\I_{6} & 0 & {- I_{6}} & \; & \vdots \\\vdots & \vdots & \; & \ddots & 0 \\I_{6} & 0 & \ldots & 0 & {- I_{6}}\end{bmatrix} \in {\mathbb{R}}^{6\; n_{p} \times 6{({n_{p} - 1})}}}} & (23)\end{matrix}$

Also, the Jacobian of all the fixed joints in the split system isdescribed in Equation 24, as follows:

$\begin{matrix}{F = {\begin{bmatrix}F_{1} & \; & 0 \\\; & \ddots & \; \\0 & \; & F_{n}\end{bmatrix}.}} & (24)\end{matrix}$

Assigning the Contacts to the Split Bodies

Contacts are assigned to sub-bodies on a one-to-one basis. That is,contacts in the set of n_(β) contacts acting on body β are assigned,such that each contact is assigned to a corresponding sub-body. Theoriginal Jacobian is partitioned into m×n blocks, such that J_(αβ) ϵ

^(I×6) represents the effect of contact α on body β, as described inEquation 25, as follows:

$\begin{matrix}{J = {\begin{bmatrix}J_{11} & \ldots & J_{1\; n} \\\vdots & \; & \vdots \\J_{m\; 1} & \ldots & J_{mn}\end{bmatrix}.}} & (25)\end{matrix}$

Each J_(β) has n_(β) nonzero rows corresponding to the contacts thataffect body β. Let r_(j) be the index of the jth nonzero row. In oneimplementation, the jth constraint of body β is assigned to the jthsub-body of body β, as expressed in Equation 26, as follows:

$\begin{matrix}{\left\lbrack C_{\alpha\beta} \right\rbrack_{1,j} = \left\{ {\begin{matrix}J_{\alpha\beta} & {{{if}\mspace{14mu} \alpha} = r_{j}} \\0 & {otherwise}\end{matrix}.} \right.} & (26)\end{matrix}$

The expression, [M_(αβ)]_(ij) is used to represent element i,j of blockα, β of any block matrix M. Later, the inverse map of Equation 26 isneeded. That is, given a split contact Jacobian, C, the correspondingunsplit contact Jacobian, J needs to be found. This is accomplishedusing a couple more definitions. For instance, let s_(α,1) be the rankof contact α in its first body's constraint list, and s_(α,2) be therank in its second. As such, the inverse map is now expressed inEquation 27, as follows:

$\begin{matrix}{J_{\alpha\beta} = \left\{ {\begin{matrix}\left\lbrack C_{\alpha\beta} \right\rbrack_{1,x_{\alpha,1}} & {{{if}\mspace{14mu} b_{\alpha,1}} = \beta} \\\left\lbrack C_{\alpha\beta} \right\rbrack_{1,x_{\alpha,2}} & {{{if}\mspace{14mu} b_{\alpha,2}} = \beta}\end{matrix}.} \right.} & (27)\end{matrix}$

Putting all the bodies back together, the contact Jacobian for the wholesplit system is expressed in Equation 28, as follows:

$\begin{matrix}{C = {\begin{bmatrix}C_{11} & \ldots & C_{1\; n} \\\vdots & \; & \vdots \\C_{m\; 1} & \ldots & C_{mn}\end{bmatrix}.}} & (28)\end{matrix}$

The impulse required to enforce the contact constraints on the splitbodies (i.e., ignoring the fixed joints) is now expressed in Equation29, as follows:

LCP(CWC ^(T) , q).   (29)

A Mixed LCP to Approximately Solve Contacts and Exactly Solve FixedJoints

A system is presented that contains contact constraints and bilateraljoint constraints, or fixed joint constraints. In particular, the systemis represented with a mixed LCP (MLCP) formulation, as described inEquation 30, as follows:

$\begin{matrix}{\quad{{\begin{bmatrix}z_{C} \\z_{F}\end{bmatrix} = {{MLCP}\left( {\begin{bmatrix}N_{CC} & N_{CF} \\N_{FC} & N_{CC}\end{bmatrix},\begin{bmatrix}q_{C} \\q_{F}\end{bmatrix}} \right)}},}} & (30)\end{matrix}$

More particularly, the system containing the contact constraints andbilateral joint constraints is described in the following equations, asfollows:

(N _(CC) z _(C) +N _(CF) z _(F) q _(C))≥0⊥z _(C)≥0   (31)

(N _(FC) z _(C) +N _(FF) z _(F) +q _(F))=0   (32)

The impulse required to simultaneously satisfy the contacts on the splitbodies and the joints that hold them together is expressed in Equation33, as follows:

$\begin{matrix}{\begin{bmatrix}z_{C} \\z_{F}\end{bmatrix} = {{MLCP}\left( {{\begin{bmatrix}C \\F\end{bmatrix}{W\begin{bmatrix}C \\F\end{bmatrix}}^{T}},\begin{bmatrix}q \\0\end{bmatrix}} \right)}} & (33)\end{matrix}$

This is useful because z_(C) is a solution of the original (i.e.,unsplit) system. That is, Equation 34 shows the Theorem to be proved, asfollows:

$\begin{matrix}{\begin{bmatrix}z_{C} \\z_{F}\end{bmatrix} = {\left. {{MLCP}\left( {{\begin{bmatrix}C \\F\end{bmatrix}{W\begin{bmatrix}C \\F\end{bmatrix}}^{T}},\begin{bmatrix}q \\0\end{bmatrix}} \right)}\Rightarrow z_{C} \right. = {{{LCP}\left( {{{JM}^{- 1}J^{T}},q} \right)}.}}} & (34)\end{matrix}$

More particularly, suppose that

$\quad\begin{bmatrix}z_{C} \\z_{F}\end{bmatrix}$

is a solution of the split system, resulting in Equation 35, as follows:

Cv ^(S) +q≥0⊥z _(C)≥0,   (35)

In Equation 35, the term v^(S) is the velocity as expressed in Equation36, as follows:

v ^(S) =W(C ^(T) z _(C) +F ^(T) z _(F)).   (36)

Let e(v^(S)) be the vector consisting of the velocity of the firstsub-body for each body. It is shown that z_(C) is a solution of theunsplit system. This is accomplished in two stages. First, it is shownthat if impulse z_(C) is applied to the unsplit system, then thevelocity of the bodies is e(v^(S)). Then, it is shown that thevelocities e(v^(S)) satisfy the constraints of the unsplit system. Then,applying impulse z_(C) to the unsplit system will give the followingvelocity for each body β, in Equation 37, as follows:

$\begin{matrix}\begin{matrix}{\left( {Cv}^{S} \right)_{\alpha} = {\sum\limits_{\beta = 1}^{n}\; {C_{\alpha\beta}V_{\beta}}}} \\{= {\sum\limits_{\beta = 1}^{n}{\sum\limits_{j = 1}^{n_{\beta}}\; \left\lbrack C_{\alpha\beta} \right\rbrack_{1,{j^{v}\beta}}}}} \\{= \left( {{Je}\left( v^{S} \right)} \right)_{\alpha}}\end{matrix} & (39)\end{matrix}$

This results in Equation 38, as follows:

M ⁻¹ J ^(T) z _(C) =e(v ^(S)).   (38)

Next, the velocities of the split system is shown to satisfy theconstraints of the unsplit system in Equation 39, as follows:

$\begin{matrix}\begin{matrix}{\left( {M^{- 1}J^{T}z_{C}} \right)_{\beta} = {W_{\beta}n_{\beta}{\sum\limits_{k = 1}^{n_{b}}\; \left( {C_{\beta}^{T}z_{C}} \right)_{k}}}} \\{= {{W_{\beta}\left( {I - {{F_{\beta}^{T}\left( {F_{\beta}W_{\beta}F_{\beta}^{T}} \right)}^{- 1}F_{\beta}W_{\beta}}} \right)}C_{\beta}^{T}z_{C}}} \\{= {\left\lbrack {W\left( {{C^{T}z_{C}} + {F^{T}z_{F}}} \right)}_{\beta} \right\rbrack_{I}.}}\end{matrix} & (37)\end{matrix}$

This results in the theorem in Equation 40, as follows:

Cv ^(S) +q≥0⊥z _(C)≥0,⇒JM ⁻¹ J ^(T) z _(C) +q≥0⊥z _(C)≥0.   (40)

Continuing, an MLCP can be solved by interleaving PGS iterations for thecontact constraints, and with a direct (i.e., exact) solver for thefixed joints, as shown in Algorithm 3, as follows:

Algorithm 3 MLCP solver with iterative contacts and exact joints 1:z_(C) = 0 2. z_(F) = 0 3. for all iterations do 4. for all contacts do5. z_(C) = max(0, z_(C) − D_(CC) ⁻¹(q_(C) + N_(CC)z_(C) + N_(CF)z_(F)))6. end for 7. z_(F) = linsolve(N_(FP), q_(F) + N_(FC)z_(C)) 8. end for

Proof of convergence for the iterative MLCP with exact joints forAlgorithm 3 is described. In particular, an LCP iteration is applied,wherein λ=1, and ω=1 in Equation 41, as follows:

$\begin{matrix}{{E = \begin{bmatrix}D_{CC}^{- 1} & 0 \\0 & N_{FF}^{- 1}\end{bmatrix}},{K = {\begin{bmatrix}L_{CC} & 0 \\N_{FC} & 0\end{bmatrix}.}}} & (41)\end{matrix}$

A new projection operator P is defined that applies (+) only to z_(C).This gives the iteration, as described in Equations 42 and 43, asfollows:

$\begin{matrix}{{z_{C}^{r + 1} = \left( {z_{C}^{r} - {D_{CC}^{- 1}\left( {q_{C} + {L_{CC}z_{C}^{r + 1}} + {\left\lbrack {U_{CC}N_{CF}} \right\rbrack \begin{bmatrix}z_{C} \\Z_{F}\end{bmatrix}}^{r}} \right)}} \right)^{+}},} & (42) \\{z_{F}^{r + 1} = {z_{F}^{r} - {{N_{FF}^{- 1}\left( {q_{F} + {\left\lbrack {N_{FC}\mspace{14mu} N_{FF}} \right\rbrack \begin{bmatrix}z_{C}^{r + 1} \\z_{F}^{r}\end{bmatrix}}} \right)}.}}} & (43)\end{matrix}$

This is the iteration that is evaluated by Algorithm 3. To proveconvergence, for all y≥0, the following must be shown, as described inEquation 44, as follows:

(z ^(P) −z)^(T) E ⁻¹(z ^(P) −y)≥0.   (44)

This is proved in Equation 45, as follows:

$\begin{matrix}\begin{matrix}{{\left( {z^{P} - z} \right)^{T}{E^{- 1}\left( {z^{P} - v} \right)}} = {{\begin{bmatrix}{z_{C}^{+} - z_{C}} \\{z_{F} - z_{F}}\end{bmatrix}^{T}\begin{bmatrix}D_{CC} & 0 \\0 & N_{FF}\end{bmatrix}}\begin{bmatrix}{z_{C}^{+} - y_{C}} \\{z_{F} - y_{F}}\end{bmatrix}}} \\{= {\left( {z_{C}^{+} - z_{C}} \right)^{T}{D_{CC}\left( {z_{C}^{+} - y_{C}} \right)}}} \\{\geq 0}\end{matrix} & (45)\end{matrix}$

Continuing, considering the specific MLCP arising from contact JacobianC and joint Jacobian F, as described in Equation 46, as follows:

$\begin{matrix}{\begin{bmatrix}z_{C} \\z_{F}\end{bmatrix} = {{{MLCP}\left( {{\begin{bmatrix}C \\F\end{bmatrix}{W\begin{bmatrix}C \\F\end{bmatrix}}^{T}},\begin{bmatrix}{Cv}^{SO} \\0\end{bmatrix}} \right)}.}} & (46)\end{matrix}$

As with PGS, the MLCP is optimized in one embodiment by introducing anauxiliary variable, as described in Equation 47, as follows:

$\begin{matrix}{v^{S} = {{{w\left\lbrack {C^{T}\mspace{14mu} F^{T}} \right\rbrack}\begin{bmatrix}z_{C} \\z_{F}\end{bmatrix}} + v^{SO}}} & (47)\end{matrix}$

This results in Algorithm 4, as described below:

Algorithm 4 Velocity MLCP solver 1: for all iterations do 2. v^(S) =PGSiter(z_(C), CWC^(T), C, v^(S)) 3. z_(F) = linsolve(FWF^(T), Fv^(S))4. v^(S) = v^(S) + WF^(T)z_(F) 5.  end for

Solving the Split System

The split system includes contact impulses that satisfy the originalsystem, but the system can be reduced in embodiments of the presentdisclosure. The system is reduced in size through transformation.Algorithm 4 is applied to the split system, which results in Algorithm5, provided below. This allows for early stoppage of the iterativeprocess to give an approximately solution to the contact constraints,while exactly solving the fixed joint constraints.

The first part applies a single iteration of PGS to all the contacts.Each sub-body has exactly one constraint, so the contacts can be safelycomputed concurrently on m threads. Equations 48 and 49 update thevelocity of body j of contact α, as follows:

i=d _(α,j)   (48)

[v _(β) ^(S)]_(i) =[v _(β) ^(S)]_(i) +[W _(β)]_(i) [C _(α,β)]_(i) ^(T)(z_(α) ^(C) −t).   (49)

The fixed joints could be solved with a direct linear solver, but thiswould take too much memory and time. However, in embodiments of thepresent disclosure, the fixed joints can be solved using a lower-costanalytical solution, as expressed in Equation 50, as follows:

vi ϵ [1 . . . n _(β)], [(v ^(S) −WF ^(T)(FWF ^(T))⁻¹ Fv ^(S))_(β)]_(i)=n _(β) ⁻¹Σ_(k=1) ^(n) ^(β) [V _(β) ]k.   (50)

Equation 50 shows that the fixed joints can be enforced by averaging thesub-body velocities of each body at each iteration. This is proven inthe following discussion, wherein for notational convenience, let S(n,m)be a matrix of size (6n, 6m) whose elements are all one, and let I(n,m)be the identify matrix of the same size, where it is defined thatS(n)=S(n,n) and I(n)=I(n,n). This results in Equation 51, as follows:

F _(β) =[S(n _(β)−1,1)−I(n _(β)−1)].   (51)

The following proof makes use of the identity, as described in Equation52, as follows:

S(n,m)S(m,p)=mS(n,p).   (52)

What is proven is shown in Equation 53, as a Theorem, as follows:

(I(n _(β))−F _(β) ^(T)(F _(β) W _(β) F _(β) ^(T))⁻¹ F _(β) W _(β))=n_(β) ⁻¹ S(n _(β))   (53)

Equation 54 starts the proof, as follows:

$\begin{matrix}{\begin{matrix}{\left( {F_{\beta}W_{\beta}F_{\beta}^{T}} \right)^{- 1} = {n_{\beta}^{- 1}{M_{\beta}\left( {{I\left( {n_{\beta} - 1} \right)} + {S\left( {n_{\beta} - 1} \right)}} \right)}^{- 1}}} \\{= {n_{\beta}^{- 1}{M_{\beta}\left( {{I\left( {n_{\beta} - 1} \right)} - {n_{\beta}^{- 1}{S\left( {n_{\beta} - 1} \right)}}} \right)}}}\end{matrix}.} & (54)\end{matrix}$

In addition, Equation 55 shows the following:

F _(β) ^(T)(I(n _(β)−1)−n _(β) ⁻¹ S(n _(β)−1))F _(β) =I(n _(β))−n _(β)⁻¹ S(n _(β)).   (55)

As such, Equation 56 results, wherein the velocities are averaged, asfollows:

I(n _(β))−F _(β) ^(T)(F _(β) W _(β) F _(β) ^(T))⁻¹ F _(β) W _(β))=I(n_(β))−n _(β) ⁻¹ M _(β) F _(β) ^(T)(I(n _(β)−1)−n _(β) ⁻¹ S(n _(β)−1))F_(β) W _(β) =n _(β) ⁻¹ S(n _(β))   (56)

As a corollary, the fixed joints of body β is solved by setting thevelocity of all its sub-bodies i ϵ [1, n_(β)] as follows in Equation 57:

$\begin{matrix}{\left\lbrack \left( {v^{S} - {{{WF}^{T}\left( {FWF}^{T} \right)}^{- 1}{Fv}^{S}}} \right)_{\beta} \right\rbrack_{i} = {{n\;}_{\beta}^{- 1}{\sum\limits_{k = 1}^{n_{\beta}}{\left\lbrack V_{\beta} \right\rbrack {k.}}}}} & (57)\end{matrix}$

Continuing, fixed joints are enforced by averaging the sub-bodyvelocities of each body at each iteration, as applied in Algorithm 5, asfollows:

Algorithm 5 Parallel PGS contacts with averaging for joints  1. p =diag(CWC^(T))⁻¹  2. for all iterations do  3. {Apply one iteration ofPGS to the sub-body contacts}  4. for all contacts, α do {in parallel} 5. t = z_(Cα)  6. z_(Cα) = max(0, z_(Cα) − Pα(Cv^(S))_(α)  7. for all j∈ {1,2} do  8. β = b_(α,j)  9. i = s_(α,j) 10. [v_(β) ^(S)]_(i) = [V_(β)^(S)]_(i) + [W_(β)]_(i)[C_(α,β)]_(i) ^(T) (z_(α) ^(C) − t) 11. end for12. end for 13. {Solve the fixed joints by velocity averaging} 14. forall bodies, β do 15. a = n_(β) ⁻¹Σ_(k−1) ^(n) ^(β) [v_(β)]_(k) 16. forall sub-bodies, i do {all sub-bodies get the same vel. 17. [v_(β)^(S)]_(i) = a 18. end for 19. end for 20.  end for

Also, the sub-body velocities of each body are equal at the end of eachiteration, so in one embodiment, the sub-body velocity need not beexplicitly stored for every sub-body. In fact, in one embodiment, themasses of the sub-bodies are scaled or calculated dynamically, as thecorresponding body mass is scaled before the sub-body mass is used.Using these optimizations and making use of the definitions of C, v^(S)and W, Algorithm 6 is provided below. In one embodiment, the algorithmis parallelizable, wherein lines 24 to 32 can be implemented using aparallel segmented reduction.

Algorithm 6 Final mass splitting solver, scalar version    1.  {Assemblemass splitting preconditioner}  2.  for all contacts, α do  3.   Pα = 0 4.   for all j ∈ {1,2} do  5.    β = b_(α,j)  6.    {divide mass bynumber of number of bodies it touches}      7.   $m_{split} = \frac{M_{\beta}}{n_{\beta}}$      8.    Pα = Pα + J_(αβ) *m_(split) ⁻¹ * J_(βα) ^(T)  9.   end for 10.   Pα = Pα⁻¹ 11.  end for12.  {Solver} 13.  for all iterations do 14.   {Solve each sub-bodycontact} 15.   for all contacts, α do {in parallel} 16.    t = z_(Cα)17.    z_(Cα) = max(0, z_(Cα−Pα)(Jv)_(α)) 18.    for all j ∈ {1,2} do19.     β = b_(α,j) 20.     D_(αj) = v_(β) + n_(β)M_(β)⁻¹[J_(αβ)]^(T)(z_(α) ^(C) − t) 21.    end for 22.   end for 23.   {Fixedjoints, implement with parallel segmented reduction} 24.   for allbodies, β do 25.    a = 0 26.    for all j ∈ {1,2} do 27.     for all isuch that b_(ij) = β do 28.      a = a + D_(ij) 29.     end for 30.   end for 31.    v_(β) = n_(β) ⁻¹a 32.   end for 33.  end for

Block Method and System for a Rigid Body Solver

The scalar rigid body solver described in FIGS. 3 and 4 so far meets thejitter and load balancing requirements. To make the solution provided bythe solver descried in FIGS. 3 and 4 converge more quickly, constraintsare solved in blocks, in one embodiment as is described in FIGS. 7 and8. In the scalar method described previously in relation to FIGS. 3-6,the bodies are split so that each sub-body had exactly one constraint.In the present embodiment, the collision detection system takes pairs ofpotentially colliding bodies and finds their intersections. For convexpairs, the intersection set has one connected component, for non-convexpairs the intersection can have many components. Each component of eachintersection is referred to as a patch. In the block solver of thepresent embodiment, the bodies are split so that so that there is onesub-body per patch. As such, each sub-body now has multiple contacts, socontacts cannot be processed in parallel. Instead, each patch isprocessed in parallel, and the contents are solved within each pathsequentially on its thread using PGS.

FIG. 7 is a block diagram of a linear complementarity, paralleliterative rigid body solver 700 that implements a block method forsimulating complex systems coming to rest while avoiding artifacts suchas, jittering and swimming at very low iteration counts, in accordancewith one embodiment of the present disclosure. In one embodiment, solver700 performs the functions of solver 250 of FIG. 2 within the rigid bodyphysics system or engine 200.

Solver 700 includes a patch determining module 710. For a particularoriginal object, a pair of potentially colliding bodies defines anintersection set of contacts. That is, a patch defines a plurality ofcontact constraints affecting the pair of colliding objects. One of thepair of colliding objects includes the original object, which isassociated with an original mass. In one embodiment, the intersectionset has one connected component (e.g., for convex pairs). In anotherembodiment, the intersection set has multiple components (e.g., fornon-convex pairs).

Solver 700 includes an object and mass splitter 720, for splitting anoriginal object into a plurality of sub-bodies. An original object issplit so that there is one sub-body per patch. As such, each sub-bodyhas multiple contacts. Instead of processing the contacts in parallel,patches are processed in parallel. As such, object splitter 720 isconfigured to split the original object by a total number of patches.Also, each sub-body has a mass, which is defined by the original massdivided by the total number of patches.

Contact constraint solver 730 is configured to solve contact constraintsapproximately for each sub-body. Further, each sub-body has multiplecontacts and/or multiple contact constraints. In one embodiment, eachcontact constraint within a patch is solved approximately andsequentially. By solving for the contact constraints within a patch, asub-body position and/or velocity is determined.

Solver 700 also includes a fixed joint solver 740 that enforces fixedjoint constraints exactly, such that positions of each sub-body areidentical. The fixed joint solver 740 is necessary because applying animpulse at a contact of the original body results in velocities at othercontacts of the same body. The goal is for each of the sub-bodies tobehave exactly like the original body, such that when an impulse isapplied at one sub-body, it is necessary to change the velocities ofother sub-bodies of the same body. The requirement that all thesub-bodies move together is modeled as a fixed joint, and is enforced bythe fixed joint solver 740. In one embodiment, the fixed joint isenforced or solved at each iteration by averaging the sub-bodyvelocities and applying the average velocity to each of the sub-bodies.The average velocity is used as the initial velocity of the sub-bodiesin the next iteration to solve contact constraints and fixed jointconstraints

FIG. 8 is a flow diagram 800 illustrating a computer implemented blockmethod for solving linear complementarity iterative rigid body dynamics,in accordance with one embodiment of the present disclosure. In anotherembodiment, flow diagram 400 is implemented within a computer systemincluding a processor and memory coupled to the processor and havingstored therein instructions that, if executed by the computer systemcauses the system to execute a block method for solving linearcomplementarity iterative rigid body dynamics. In still anotherembodiment, instructions for performing a method are stored on anon-transitory computer-readable storage medium havingcomputer-executable instructions for causing a computer system toperform a block method for solving linear complementarity iterativerigid body dynamics. In one embodiment, flow diagram 800 is implementedby solver 700 of FIG. 7, and/or solver 250 of FIG. 2. More particularly,flow diagram 800 is implemented to take a set of constraints that thepositions and/or velocities of objects must satisfy and calculate a setof forces and/or impulses such that the objects will satisfy theconstraints once the forces and/or impulses are applied to the objects.

The method of flow diagram 800 is implemented after a set of constraintsare defined, wherein contact constraints affect one or more objects orbodies. For instance, constraint generator 220 of FIG. 2 is able todetermine the constraints that affect one or more objects or bodies.Once the constraints are defined, the block method of flow diagram 800is implemented to determine the positions and/or velocities of the oneor more objects or bodies.

At 810, determining one or more patches associated with an originalobject is performed. The original object has an original mass. Also,each patch comprises a pair of colliding objects, one of which includesthe original object. Further, each patch defines a plurality of contactconstraints that affect the pair of colliding objects. As such, eachpatch is associated with multiple contacts.

At 820, splitting the original object is performed. In particular, theoriginal object is split into a plurality of sub-bodies, such that thereis one sub-body per patch. As such, the number of patches defines thenumber of sub-bodies. In addition, the mass for each sub-body is definedby dividing the original mass for the original object by the totalnumber of patches.

At 830, solving patches for their corresponding contact constraints isperformed. In particular, for each patch, contact constraints are solvedsequentially. That is, for each contact constraint of a patch, animpulse is calculated to satisfy the contact constraint. This isaccomplished by solving for a sub-body velocity vector of the sub-bodydue to the contact constraint. In one embodiment, the relative velocityvector is constrained to be resting or separating (non-negative). Also,the impulses are constrained to be non-attractive (non-negative). Inaddition, impulse should be zero at separating contacts. This isperformed until a final velocity and position is determined for thesub-body associated with the patch.

At 840, enforcing fixed joints exactly is performed, such that fixedjoint constraints are solved exactly. More particularly, positionsand/or velocities of each sub-body are solved such that they aresubstantially identical. For instance, in one embodiment, the enforcingof fixed joints is performed by averaging a plurality of sub-bodyvelocity vectors corresponding to the plurality of sub-bodies. As such,an average velocity vector is defined. Further, the average velocityvector is assigned to each of the plurality of sub-bodies.

In one embodiment, the operations in 830 and 840 are iterativelyperformed. That is, for each iteration, given a starting averagevelocity vector from the previous iteration, a new average velocityvector and new position are determined for a corresponding sub-body.These new values again correspond to an average velocity vector and/orposition for the original object that can be used finally or for thenext iteration.

In another embodiment, a frictional force is applied to the sub-bodiesat each iteration. Embodiments of the present disclosure support anymeans for applying friction to the block method for solving linearcomplementarity iterative rigid body dynamics. Embodiments of thepresent disclosure apply a coulomb friction model, though other frictionmodels are supported. For instance, friction may be considered byapplying a friction model, such that the more force an object isexerting on a surface, the harder it needs to be pushed to move itacross the surface. As such, heavier objects are harder to push thanlighter objects. In addition, the transition from sticking (e.g., whenthe object does not move) to sliding (e.g., when the object first moves)is considered.

The solution for the block method for performing linear complementarityin parallel for an iterative rigid body solver is described below. Aspreviously described, the block method simulates complex systems comingto rest while avoiding artifacts such as, jittering and swimming at verylow iteration counts. The block method converges more quickly than thescalar method previously described in relation to FIGS. 3-6.

For definition, let b_(i,1) ^(P) and b_(i,2) ^(P) be the two bodiesconstrained by patch i. Also, let n_(β) ^(P) be the number of patchesaffecting body β in the unsplit system, as described in Equation 58, asfollows:

n _(β) ^(P) =|{i:b _(i,1) ^(P) =βv b _(i,2) ^(P)=β}|.   (58)

Splitting the Bodies in the Block Method

The bodies are split into sub-bodies so that each sub-body has onepatch. The total number of sub-bodies is described in Equation 59, asfollows:

$\begin{matrix}{n_{\beta} = {\sum\limits_{\beta = 1}^{n}{n_{\beta}^{P}.}}} & (59)\end{matrix}$

Again, the mass of each sub-body is split evenly between its sub-bodies,in one embodiment, as described in Equation 60, as follows:

W β P = [ n β P  M β - 1 0 ⋱ 0 n β P  M β - 1 ] ∈ 6  n β P × 6  n βP ( 60 )

Joining the Bodies Back Together

The sub-bodies of each body are joined together with n_(β) ^(P)−1 fixedjoints. The Jacobian of the fixed joints of body β is described inEquation 61, as follows:

F β P = [ I 6 - I 6 0 … 0 I 6 0 - I 6 ⋮ ⋮ ⋮ ⋱ 0 I 6 0 … 0 - I 6 ] ∈ 6 n β P × 6  ( n β P - 1 ) . ( 61 )

Assigning the Contacts to the Split Bodies

Let the total number of patches be p and the number of contacts in patchi be m_(i). The rows of J are partitioned into blocks as well as thecolumns, so that J_(iβ) ^(P) ϵ

^(m) ^(i) ^(×6) represents the effect of patch i on body β as shown inEquation 62, as provided below:

$\begin{matrix}{J = {\begin{bmatrix}J_{11}^{P} & \ldots & J_{1n}^{P} \\\vdots & \; & \vdots \\J_{p\; 1}^{P} & \ldots & J_{pn}^{P}\end{bmatrix}.}} & (62)\end{matrix}$

In addition, each patch of a number of patches n_(β) ^(P) that is actingon body β is assigned to its own sub-body, as described in Equation 63,as follows:

$\begin{matrix}{\left\lbrack C_{\alpha\beta}^{P} \right\rbrack_{i,j} = \left\{ {\begin{matrix}\left\lbrack J_{\alpha\beta}^{P} \right\rbrack_{i,j} & {{{if}\mspace{14mu} r_{j}} = \alpha} \\0 & {otherwise}\end{matrix}.} \right.} & (63)\end{matrix}$

Solving the Block Split System

A block-sparse MLCP is defined in Equation 64, as follows:

$\begin{matrix}{\begin{bmatrix}z_{C}^{P} \\z_{F}^{P}\end{bmatrix} = {{{{MLCP}\left( {\begin{bmatrix}N_{CC}^{P} & N_{CF}^{P} \\N_{FC}^{P} & N_{FF}^{P}\end{bmatrix},\begin{bmatrix}q \\0\end{bmatrix}} \right)}\begin{bmatrix}N_{CC}^{P} & N_{CF}^{P} \\N_{FC}^{P} & N_{FF}^{P}\end{bmatrix}} = {\begin{bmatrix}C^{P} \\F^{P}\end{bmatrix}{W^{P}\begin{bmatrix}C^{P} \\F^{P}\end{bmatrix}}^{T}}}} & (64)\end{matrix}$

In Equation 64, the term N_(CC) ^(P) is a block diagonal with blocksdefined in Equation 65, as follows:

$\begin{matrix}\begin{matrix}{B_{\alpha\alpha} = {\sum\limits_{k = 1}^{n}{C_{\alpha \; k}^{P}{W_{k}\left( C_{\alpha \; k}^{P} \right)}^{T}}}} \\{= {\sum\limits_{k = 1}^{n}{n_{k}^{P}J_{\alpha \; k}^{P}{M_{k}^{- 1}\left( J_{\alpha \; k}^{P} \right)}^{T}}}} \\{= {{n_{b_{\alpha,1}}J_{\alpha,b_{\alpha,1}}^{P}{M_{b_{\alpha,1}}^{- 1}\left( J_{\alpha,b_{\alpha,1}}^{P} \right)}^{T}} + {n_{b_{\alpha,2}}J_{\alpha,b_{\alpha,2}}^{P}{{M_{b_{\alpha,2}}^{- 1}\left( J_{\alpha,b_{\alpha,1}}^{P} \right)}^{T}.}}}}\end{matrix} & (65)\end{matrix}$

Let L_(αα) and D_(αα) be the lower triangle and diagonal of B_(αα).Applying the LCP iteration, with λ=1 and ω=1 results in Equations 66-68,as follows:

$\begin{matrix}{E = {\begin{bmatrix}D_{11} & \ldots & 0 & 0 \\\; & \ddots & \; & \vdots \\0 & \; & D_{pp} & 0 \\0 & \ldots & 0 & N_{FF}^{P}\end{bmatrix}\mspace{14mu} {and}}} & (66) \\{K = {\begin{bmatrix}L_{11} & \; & 0 & 0 \\\; & \ddots & \; & \vdots \\0 & \; & L_{pp} & 0 \\\left( N_{FC}^{P} \right)_{1} & \ldots & \left( N_{FC}^{P} \right)_{P} & 0\end{bmatrix}\mspace{14mu} {and}}} & (67) \\\begin{matrix}z_{C\; 1}^{r + 1} & {\left( {z_{1}^{r} - {D_{11}^{- 1}\left( {q_{1} + {L_{11}z_{1}^{r + 1}} + {U_{11}z_{1}^{F}} + \left( N_{CF}^{P} \right)_{1Z_{F}}} \right)}} \right) +} \\\vdots & \vdots \\{z_{Cp}^{r + 1} =} & {\left( {z_{p}^{r} - {D_{pp}^{- 1}\left( {q_{p} + {L_{pp}z_{p}^{r + 1}} + {U_{pp}z_{p}^{F}} + \left( N_{CF}^{P} \right)_{{pZ}_{F}}} \right)}} \right) + -} \\z_{F}^{r + 1} & {\left( N_{FF}^{P} \right)^{- 1}{\sum\limits_{\alpha = 1}^{P}{\left( N_{FC}^{P} \right)_{\alpha}\left( z_{C} \right)_{\alpha}^{r + 1}}}}\end{matrix} & (68)\end{matrix}$

Algorithm 7 provides an implementation of this iteration. In oneembodiment, the blocks of z_(C) are computed in parallel.

Algorithm 7 Block MLCP solver with iterative contacts and exact joints1: z_(C) = 0 2.  z_(F) = 0 3.  for all iterations do 4. for all blocks,α do {in parallel} 5. for all contacts in block, i do 6. t =[z_(Cα)]_(i) − [D_(αα)]_(ii) ⁻¹([q_(α)]_(i) + [B_(αα)z_(Cα)]_(i) +[N_(CF) ^(P)z_(F))_(α)]_(i) 7. [z_(Cα)]_(i) = max(0, t) 8. end for 9.end for 8. z_(F) = linsolve(N_(FP), q_(F) + N_(FC)z_(C)) 8.  end for

Lines 4-7 of Algorithm 7 are equivalent to running PGS on each blockindependently. As an improvement, Algorithm 8 is a velocity version ofAlgorithm 7, as described below:

Algorithm 8 Velocity MLCP block solver 1: for all iterations do 2. forall blocks, α do {in parallel} 3. v^(S) = PGSiter(z_(α), D_(αα), [C_(α1)... C_(αn)], v^(S)) 4. end for 3. z_(F) = linsolve(N_(PF), N_(FC)z_(C))4. v^(S) = v^(S) + WF^(T)z_(F) 5.  end for

The fixed joints are solved by velocity averaging, as previouslydescribed in relation to the scalar method for solving rigid bodydynamics. Algorithm 9 provides the velocity averaging for the fixedjoints below.

Algorithm 9 Block solver with velocity averaging for joints  1. for alliterations do  2. for all patches, α do {in parallel}  3. for allcontacts in patch, i do {sequentially on thread α}  4. t = [z_(Cα)]_(i) 5. [z_(Cα)]_(i) = max(0, [z_(Cα)]_(i) − [D_(αα)]_(ii) ⁻¹ [C_(α1) ...C_(αn)]v_(i) ^(S))  6. for all j ∈ {1,2} do  7. β = b_(i,j)  8. k =s_(α,j)  9. [v_(β) ^(S)]_(k) = [v_(β) ^(S)]_(k) +[w_(β)]_(k)[C_(α,β)]_(k) ^(T)(z_(Cα) − t) 10. end for 11. end for 12. end for 13.  {Solve the fixed joints by velocity averaging} 14.  forall bodies, β do 15. a = n_(β) ⁻¹Σ_(k−1) ^(n) ^(β) [V_(β)]_(k) 16. forall sub-bodies, i do {all sub-bodies get the same vel. 17. [v_(β)^(S)]_(i) = a 18. end for 19. end for 20.  end for

For the definitions of C^(P) and W^(P), Equation 69 is provided below:

diag(B _(αα))=diag([J _(α1) ^(P) . . . J _(αn) ^(P) ]M ⁻¹ [J _(α1) ^(P). . . J _(αn) ^(P)]^(T)).   (69)

Finally, the sub-body velocities are eliminated in the same way aspreviously described in the scalar method for solving rigid bodydynamics, as described in Algorithm 10, as follows:

Algorithm 10 Final mass splitting solver, block version    1.  {Assemblemass splitting preconditioner}  2.  for all patches, α do  3.   for allcontacts in patch, i do  4.    [Pα]_(i) = 0  5.    for all j ∈ {1,2} do 6.     β = b_(α,j) ^(P)  7.     {divide body's mass by its patch count}    8.     $m_{split} = \frac{M_{\beta}}{n_{\beta}^{P}}$     9.     [Pα]= [Pα]_(j) + [j_(αβ)]_(i) * m_(split) ⁻¹ * [j_(αβ)]_(i) ^(T) 10.    endfor 11.    Pα = Pa⁻¹ 12.   end for 13.  end for 14.  {Solver} 15.  forall iterations do 16.   for all patches (α do {in parallel} 17.    β₁,β₂ = b_(α1) ^(,P), b_(α2) ^(,P) 18.    D_(αj), D_(α2) = v_(β) ₁ , v_(β)₂ 19.    for all contacts in patch, i do {sequentially on thread α } 20.    t = [z_(Cα)]_(i) 21.     [z_(Cα)]_(i) = max(0, [z_(Cα)]_(i) −[Pα]i(J_(α,β) ₁ D_(α1) + J_(α,β) ₂ D_(α2))) 22.     for all j ∈ {1,2} do23.      k = s_(α,j) 24.      D_(αj) = D_(aj) + M_(β) _(j) ⁻¹, J_(α,β)_(j) ^(T)(z_(Cα) − t) 25.     end for 26.    end for 27.   end for 28.  {Fixed joints, implement with parallel segment4ed reduction] 29.   a =0 30.   for all j ∈ {1,2} do 31.    for all α such that β_(αj) = β do32.    a = a + D_(αj) 33.    end for 34.   end for 35   v_(β) = n_(β)⁻¹a 36.  end for

Thus, according to embodiments of the present disclosure, systems andmethods are described that provide for a parallel iterative rigid bodysolver and method for implementing the same that simulate complexsystems coming to rest while avoiding artifacts such as, uttering andswimming at very low iteration counts. The linear complementaritysolvers and methods for implementing the same for formulating rigid bodydynamics provide for correct momentum propagation, static friction,dynamic friction, and stable resting contact.

While the foregoing disclosure sets forth various embodiments usingspecific block diagrams, flowcharts, and examples, each block diagramcomponent, flowchart step, operation, and/or component described and/orillustrated herein may be implemented, individually and/or collectively,using a wide range of hardware, software, or firmware (or anycombination thereof) configurations. In addition, any disclosure ofcomponents contained within other components should be considered asexamples because many other architectures can be implemented to achievethe same functionality.

The process parameters and sequence of steps described and/orillustrated herein are given by way of example only and can be varied asdesired. For example, while the steps illustrated and/or describedherein may be shown or discussed in a particular order, these steps donot necessarily need to be performed in the order illustrated ordiscussed. The various example methods described and/or illustratedherein may also omit one or more of the steps described or illustratedherein or include additional steps in addition to those disclosed.

While various embodiments have been described and/or illustrated hereinin the context of fully functional computing systems, one or more ofthese example embodiments may be distributed as a program product in avariety of forms, regardless of the particular type of computer-readablemedia used to actually carry out the distribution. The embodimentsdisclosed herein may also be implemented using software modules thatperform certain tasks. These software modules may include script, batch,or other executable files that may be stored on a computer-readablestorage medium or in a computing system. These software modules mayconfigure a computing system to perform one or more of the exampleembodiments disclosed herein. One or more of the software modulesdisclosed herein may be implemented in a cloud computing environment.Cloud computing environments may provide various services andapplications via the Internet. These cloud-based services (e.g.,software as a service, platform as a service, infrastructure as aservice, etc.) may be accessible through a Web browser or other remoteinterface. Various functions described herein may be provided through aremote desktop environment or any other cloud-based computingenvironment.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as may be suited to theparticular use contemplated.

Embodiments according to the present disclosure are thus described.While the present disclosure has been described in particularembodiments, it should be appreciated that the disclosure should not beconstrued as limited by such embodiments, but rather construed accordingto the below claims.

1. A method for solving linear complementarity problems for rigid bodysimulation, the method comprising: determining a first number of contactconstraints corresponding to an object; splitting the object into asecond number of sub-bodies corresponding to the first number of contactconstraints; assigning a contact constraint from the contact constraintsto each sub-body of the sub-bodies; and solving the contact constraintssuch that at least one contact constraint for at least one sub-body issolved independently from at least one other contact constraint for atleast one other sub-body of the sub-bodies.
 2. The method of claim 1,wherein the splitting the object is executed by a parallel iterativerigid body solver executed using one or more processors of a computersystem.
 3. The method of claim 1, wherein each sub-body of thesub-bodies includes an identical spatial extent with respect to eachother sub-body.
 4. The method of claim 1, wherein each sub-body of thesub-bodies includes an identical center of mass with respect to eachother sub-body.
 5. The method of claim 1, wherein each sub-body of thesub-bodies includes an identical initial velocity with respect to eachother sub-body
 6. The method of claim 1, wherein at least two or morecontact constraints of the contact constraints are solved in parallelusing two or more threads of a processor.
 7. The method of claim 1,further comprising modeling the sub-bodies as a fixed joint.
 8. Themethod of claim 7, wherein the modeling the sub-bodies as the fixedjoint includes assigning an average velocity to each sub-body of thesub-bodies to cause at least one of positions or velocitiescorresponding to each sub-body to be identical with respect to eachother sub-body.
 9. The method of claim 1, wherein each sub-body includesa mass equal to an original mass of the object divided by the secondnumber of sub-bodies.
 10. The method of claim, wherein the determiningthe first number of contact constraints includes determining a thirdnumber of discrete objects associated with the contact constraints,further wherein the first number is determined to be the third number.11. A system comprising: a computing device including one or moreprocessing devices and one or more memory devices communicativelycoupled to the one or more processing devices storing programmedinstructions thereon that are executed by the one or more processingdevices to execute a parallel iterative rigid body solver that causesthe instantiation of: an object splitter to: determine a first contactconstraint and a second contact constraint with respect to an object;and based at least in part on the first contact constraint and thesecond contact constraint, split the object into a first sub-body and asecond sub-body; a contact constraint assigner to assign the firstcontact constraint to the first sub-body and the second contactconstraint to the second sub-body; and a contact constraint solver tosolve the first contact constraint and the second contact constraint.12. The system of claim 11, wherein the object, the first sub-body, andthe second sub-body have identical spatial extents.
 13. The system ofclaim 11, wherein the first sub-body and the second sub-body haveidentical centers of mass.
 14. The system of claim 11, wherein the firstsub-body and the second sub-body have identical initial velocities. 15.The system of claim 11, wherein the first contact constraint and thesecond contact constraint are solved at least partially in parallel. 16.The system of claim 15, wherein the first contact constraint is solvedusing one or more first threads of a processor of the one or moreprocessors and the second contact constraint is solved using one or moresecond threads of a processor different from the one or more firstthreads.
 17. The system of claim 11, further comprising a fixed jointsolver to model the first sub-body and the second sub-body as a fixedjoint.
 18. The system of claim 11, wherein the first sub-body and thesecond sub-body include a mass equal to an original mass of the objectdivided by a total number of contact constraints, the total numberincluding the first contact constraint and the second contactconstraint.
 19. A method for solving linear complementarity problems forrigid body simulation, the method comprising: splitting an object into aplurality of sub-bodies corresponding to a number of individual objectscausing at least one contact constraint with the object; for eachindividual object of the individual objects: assigning contactconstraints between the individual object and the object to a sub-bodyof the plurality of sub-bodies; and solving the contact constraintscorresponding to the sub-body independently of solving other contactconstraints corresponding to at least one other sub-body of thesub-bodies.
 20. The method of claim 19, wherein each sub-body of theplurality of sub-bodies includes at least one of an identical spatialextent with respect to each other sub-body, an identical center of masswith respect to each other sub-body, or an identical initial velocitywith respect to each other sub-body.